Evidence map›Paper›PMID 35181798›Full record

SynthesisAbdominal radiology (New York)2022

Diagnostic accuracy of vesical imaging-reporting and data system (VI-RADS) for the detection of muscle-invasive bladder cancer: a meta-analysis.

Yuebo Feng, Kai Zhong, Rui Chen, Weibin Zhou

Abstract readMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuebo Feng *Department of Radiology, The People's Hospital of Jianyang City, Jianyang, Sichuan Province, China.
Kai ZhongDepartment of Radiology, The People's Hospital of Jianyang City, Jianyang, Sichuan Province, China.
Rui ChenDepartment of Radiology, The People's Hospital of Jianyang City, Jianyang, Sichuan Province, China.
Weibin ZhouDepartment of Radiology, The People's Hospital of Jianyang City, Jianyang, Sichuan Province, China. 332757057@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeVesical Imaging-Reporting and Data System (VI-RADS) was proposed and considered as a standardized reporting criterion for bladder magnetic resonance imaging (MRI). VI-RADS could suggest the likelihood of muscle invasion based on the multiparametric MRI (mp-MRI) findings which contain five-point scores. The current study is designed to comprehensively and systematically evaluate the diagnostic performance of VI-RADS (score 3 and 4) for predicting muscle invasion.

methodsThe Cochrane Library, Embase, and PubMed were searched comprehensively from inception to October 2021.

resultsFinally, 19 studies incorporating 2900 patients were enrolled. The pooled sensitivity and specificity of VI-RADS 3 for predicting muscle invasion were 0.92 (95%CI 0.89-0.94) and 0.82 (95%CI 0.76-0.87), respectively. The pooled sensitivity and specificity of VI-RADS 4 were 0.78 (95%CI 0.72-0.83) and 0.96 (95%CI 0.93-0.97), respectively. And the area under the curve (AUCs) of VI-RADS 3 and 4 were all 0.94 (95%CI 0.92-0.96). No significant publication biases were not observed for VI-RADS 3 (P = 0.74) and 4 (P = 0.57).

conclusionThe VI-RADS reveals a good diagnostic performance for predicting muscle invasive in bladder cancer, which also has good clinical utilities and applicability. And VI-RADS 3 and 4 as cutoff values provide similar overall diagnostic and could be selectively applied individually. Prospective studies with a large scale are further required to validate the accuracy of the VI-RADS score.

Indexed as

Multiparametric Magnetic Resonance ImagingUrinary Bladder NeoplasmsHumansMagnetic Resonance ImagingMusclesProspective StudiesUrinary BladderBladder cancerMagnetic resonance imagingMeta-analysisMuscle invasionVesical imaging-reporting and data system

Identifiers

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Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.